132 citations · 683 across the 107 of their papers we have counts for
15 papers · 1 filter
Deep Learning-Based Frequency Offset Estimation
Tao Chen, Shilian Zheng, Jiawei Zhu +2
In wireless communication systems, the asynchronization of the oscillators in the transmitter and the receiver along with the Doppler shift due to relative movement may lead to the…
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition
Tao Chen, Shilian Zheng, Kunfeng Qiu +3
The use of deep learning for radio modulation recognition has become prevalent in recent years. This approach automatically extracts high-dimensional features from large datasets,…
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries
Wenjie Lv, Zhen Wang, Yitao Zheng +3
Machine learning security has recently become a prominent topic in the natural language processing (NLP) area. The existing black-box adversarial attack suffers prohibitively from…
Attacking The Assortativity Coefficient Under A Rewiring Strategy
Shuo Zou, Bo Zhou, Qi Xuan
Degree correlation is an important characteristic of networks, which is usually quantified by the assortativity coefficient. However, concerns arise about changing the assortativit…
RK-core: An Established Methodology for Exploring the Hierarchical Structure within Datasets
Yao Lu, Yutian Huang, Jiaqi Nie +2
Recently, the field of machine learning has undergone a transition from model-centric to data-centric. The advancements in diverse learning tasks have been propelled by the accumul…
Can pre-trained models assist in dataset distillation?
Yao Lu, Xuguang Chen, Yuchen Zhang +7
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training. Meanwhile,…
Multi-triplet Feature Augmentation for Ponzi Scheme Detection in Ethereum
Chengxiang Jin, Jiajun Zhou, Shengbo Gong +2
Blockchain technology revolutionizes the Internet, but also poses increasing risks, particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing scams,…
MONA: An Efficient and Scalable Strategy for Targeted k-Nodes Collapse
Yuqian Lv, Bo Zhou, Jinhuan Wang +2
The concept of k-core plays an important role in measuring the cohesiveness and engagement of a network. And recent studies have shown the vulnerability of k-core under adversarial…
PathMLP: Smooth Path Towards High-order Homophily
Jiajun Zhou, Chenxuan Xie, Shengbo Gong +4
Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph n…
Subgraph Networks Based Contrastive Learning
Jinhuan Wang, Jiafei Shao, Zeyu Wang +3
Graph contrastive learning (GCL), as a self-supervised learning method, can solve the problem of annotated data scarcity. It mines explicit features in unannotated graphs to genera…
Clarify Confused Nodes via Separated Learning
Jiajun Zhou, Shengbo Gong, Xuanze Chen +4
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, c…
Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Dayuan Chen, Jian Zhang, Yuqian Lv +5
Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbat…
How does Contrastive Learning Organize Images?
Yunzhe Zhang, Yao Lu, Qi Xuan
Contrastive learning, a dominant self-supervised technique, emphasizes similarity in representations between augmentations of the same input and dissimilarity for different ones. A…
SR-init: An interpretable layer pruning method
Hui Tang, Yao Lu, Qi Xuan
Despite the popularization of deep neural networks (DNNs) in many fields, it is still challenging to deploy state-of-the-art models to resource-constrained devices due to high comp…
Neighborhood Homophily-based Graph Convolutional Network
Shengbo Gong, Jiajun Zhou, Chenxuan Xie +1
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical…